Databricks Databricks-Certified-Data-Engineer-Professional Value Pack (Frequently Bought Together)

Which of the following is true of Delta Lake and the Lakehouse?

• A. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
• B. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
• C. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
• D. Z-order can only be applied to numeric values stored in Delta Lake tables
• E. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.

Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

• A. Validates a complete use case of your application
• B. Improves the quality of your data
• C. Yields faster deployment and execution times
• D. Troubleshooting is easier since all steps are isolated and tested individually
• E. Ensures that all steps interact correctly to achieve the desired end result

A data engineer inherits a Delta table with historical partitions by country that are badly skewed.
Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve. Which two actions should the data engineer take? (Choose two.)

• A. Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.
• B. Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.
• C. Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.
• D. Periodically run OPTIMIZE table_name.
• E. Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.

The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active user. They then modify their code to the following (leaving all other variables unchanged).

Which statement describes what will happen when the above code is executed?

• A. The connection to the external table will succeed; the string "redacted" will be printed.
• B. The connection to the external table will succeed; the string value of password will be printed in plain text.
• C. The connection to the external table will fail; the string "redacted" will be printed.
• D. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.
• E. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.

A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impressions led to monetizable clicks.
In the code below, Impressions is a streaming DataFrame with a watermark ("event_time", "10 minutes")

The data engineer notices the query slowing down significantly.
Which solution would improve the performance?

• A. Joining on event time constraint: clickTime >= impressionTime - interval 3 hours and removing watermarks
• B. Joining on event time constraint: clickTime >= impressionTime AND clickTime <= impressionTime interval 1 hour
• C. Joining on event time constraint: clickTime == impressionTime using a leftOuter join
• D. Joining on event time constraint: clickTime + 3 hours < impressionTime - 2 hours